1. Field of the Invention
The present invention is directed in general to field of internal combustion engines. In one aspect, the present invention relates to a system and method for improving the combustion process in automotive systems by predicting knock signals.
2. Description of the Related Art
As gasoline supplies have decreased and become more costly, there is an increasing demand for efficient fuel consumption. In addition, concern about the environmental effects caused by combustion engines has increased demand for combustion engines with reduced engine emissions. While auto manufacturers have attempted to meet these demands by increasing the engine compression ratio, this can result in engine knocking, which is an undesirable mode of combustion which originates spontaneously and sporadically, causing increased pollution and damaging engine parts, as well as creating unpleasant metallic noises.
To increase fuel economy and reduce engine emissions, there have been attempts to sense or detect engine knocks, and then use the detected knock signals to control the engine combustion process. Typical automotive systems capture or detect a knock event using sensors, such as jerk sensors or piezoelectric or piezoceramic accelerometer sensors. For example, depending on the dimensions of the combustion chamber and on the sound velocity of the cylinder charge, in-cylinder pressure vibrations excite structure vibration pulses which can be measured by accelerometer sensors. The reverberation resonance of the cylinder lies typically between 2 kHz and 12 kHz. As will be appreciated, a rough estimate of the knock frequency for a given engine cylinder geometry is given by the following equation:
                              f          r                =                                            P              mn                        ·            C                                π            ·            B                                              (        1        )            where fr is the knock resonant frequency, Pmn is the vibration mode constant, C is the velocity of sound in the gas in the cylinder and B is the radius of the cylinder.
An example of a conventional knock detection scheme 10 is shown in FIG. 1, which depicts the different blocks in a standard knock detection algorithm, including knock sensor 11, signal pre-processing 12, parameter extraction 13, and knock detection 14. At a detection stage 11, the knock sensors help detect the knock event. The knock sensor can be a non-intrusive sensor (e.g., a simple accelerometer) or an intrusive sensor (e.g., a piezoelectric ceramic that serves as a reference for measuring the knock energy released in the resonance frequencies of the combustion chamber). The detected signal is then processed in the signal processing stage 12 in either the digital or analog domain, and parameters (such as pressure or vibration) are extracted in the extraction stage 13. There are several methods for extracting the energy of the resonance frequencies generated by engine knocks, including time-series processes, frequency processes, and time-frequency distribution (TFD) techniques for transforming uni-dimensional signals into a bi-dimensional representation as a function of time and frequency. For example, the most widely used TFD is the Short Time Fourier Transform (STFT), and researchers have also developed time-frequency distributions, such as the smoothed version of the Wigner-Ville distribution, and adapted it to the study of knock pressure signals. The representation of a signal with knock using such distributions shows both the non-stationary character of the signal, as well as the dependence of the resonance frequency values with respect to time.
As will be appreciated, there are different challenges posed to accurately detecting knock signals at the knock detection stage 14, depending on the type of sensor used and on how the parameter extraction stage is implemented. For example, magnetostrictive jerk sensors are effective in detecting knock signals, but they often have too many components to be cost-effective. On the other hand, a simple accelerometer attached directly to the head gasket or engine block can measure the intensity of the vibrations induced in the combustion chamber by engine knocking, thereby providing localized information for each cylinder. Though they are non-intrusive and easy to use, accelerometer sensors are sensitive to engine vibrations and shocks from metallic parts of the engine.
Errors can also be introduced in the parameter extraction stage. For example, one drawback associated with Short Time Fourier Transform (STFT) extraction is that the amplitude smearing effect prevents the instantaneous frequency parameter from being correctly estimated. To illustrate this problem, FIGS. 2 and 3 depict two different amplitude response measurements for a signal frequency where the y-axis is in the logarithmic scale. In FIG. 2, the amplitude response 21 is for a sinusoidal signal frequency that coincides with one of the bin frequencies of the STFT, in which case the original amplitude is retained after the STFT. However, FIG. 3 depicts the amplitude response 22 of a sinusoidal signal frequency that lies in between two adjacent bin frequencies of the STFT, in which case the energy is spread over the entire spectrum. As a consequence of the amplitude smearing effect, the detected amplitude is lower in the case. This is illustrated in FIGS. 4 and 5 which depict two different amplitude response measurements for a signal frequency where the y-axis is in the linear scale. In FIG. 4, the amplitude response 23 (approximately 510) is shown for a sinusoidal signal frequency that coincides with one of the bin frequencies of the STFT, while FIG. 5 shows that the detected amplitude response 24 (approximately 435) is lower when the peak frequency lies in between two adjacent frequency bins. In contrast to STFT schemes, complex time-frequency distributions (e.g., Wigner-Ville distributions) are computationally intensive, and can produce confusing artifacts in the case of multi-component signals which lead to errors in pitch detection.
As seen from the foregoing, conventional approaches for detecting engine knocking in combustion engines are unduly complex, expensive and unreliable. In addition, such systems do not accurately measure the signal frequency due to sensitivity to extraneous signals (such as engine vibrations and shocks) and because of amplitude smearing effects caused by conventional STFT extraction schemes. Accordingly, there is a need for an improved system and methodology for detecting knock signals in an internal combustion engine. There is also a need for an engine knock detection scheme which provides a way to accurately measure and detect engine knock events so that engine control algorithms can minimize its undesirable effects scheme. In addition, there is a need for a knock detection system and methodology which overcomes the problems in the art, such as outlined above. Further limitations and disadvantages of conventional processes and technologies will become apparent to one of skill in the art after reviewing the remainder of the present application with reference to the drawings and detailed description which follow.
It will be appreciated that for simplicity and clarity of illustration, elements illustrated in the drawings have not necessarily been drawn to scale. For example, the dimensions of some of the elements are exaggerated relative to other elements for purposes of promoting and improving clarity and understanding. Further, where considered appropriate, reference numerals have been repeated among the drawings to represent corresponding or analogous elements.